Because of the limitations of the range of operations of the sensors and cameras, research has been conducted to combine a network of cameras and sensors to observe an arbitrary large area. The goal of combining multiple camera and sensors can make a precise tracking and activity recognition in a scalable space. In most cases, the architecture of current camera networks may not be efficient because each of the sensors is activated (i.e., “ON”) during an entirety of the operation time. Furthermore, in most of the approaches, a calibration step can be required so that cameras can communicate with each other with the same base reference.
For example, OpenPTrack is an open source project launched in 2013 to create a scalable, multi-camera solution for person tracking, which helps enable several people to be tracked over large areas in real time. The project is designed for applications in education, arts, and culture, as a starting point for exploring group interaction with digital environments.
Furthermore, for some applications of the camera network, a multi-camera video abnormal behavior detection method based on a network transmission algorithm is used. The method can include the following steps: blocking a scene of a multi-camera system, and constructing a network model by taking each sub-block as a node and the behavior relevance among the sub-blocks as a weight edge.